This National Cancer Institute (CFDA 93.394 Cancer Detection and Diagnosis Research) Project Grant award of $683,089 to the Sloan-Kettering Institute for Cancer Research supports the development of an improved, interpretable deep learning algorithm called DeepLIIF for more reproducible and accurate PD-L1 immunohistochemistry (IHC) biomarker quantification. The goal is to leverage virtual multiplex immunofluorescence (MPIF) restaining and large, diverse datasets across lung and bladder cancers to...
This $435,827 Project Grant from the National Cancer Institute (NCI) under the CFDA 93.394 Cancer Detection and Diagnosis Research program supports research conducted by New York University (NYU) School of Medicine to develop deep learning methods for analyzing mass spectrometry imaging (MSI) data. The goal is to make MSI data more accessible to existing machine learning workflows by expanding the dimensionality of the data structure to treat each metabolite or lipid as an individual "color...
This Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) to Emory University for $140,511 supports the development of a computational framework that leverages AI visual explanation to guide AI-based abdominal cancer diagnostic imaging. The key objectives are to: 1) Improve AI sample efficiency through visual explanation supervision of cancer imaging annotations, 2) Consolidate AI's knowledge across multi-institutional data while...
This Project Grant award of $572,490.00 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) will fund the development of PILLAR, an AI-based tool to predict breast cancer risk from longitudinal multi-modal breast imaging data. The project aims to create novel machine learning architectures and self-supervised learning algorithms to improve the accuracy of cancer risk assessment over current clinical models. Additionally, the project will develop methods...
The National Cancer Institute (NCI) awarded a $564,414 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University. The grant will fund a 5-year research project to: 1) Develop and validate a predictive model based on electronic health record data that can accurately identify individuals at high risk for gastric cancer, and 2) Develop a mathematical model to assess the potential...
This Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) provides $406,204 to DePaul University to develop and validate a novel model of visual-semantic processing for computer-aided diagnosis (CAD) systems. The goal is to create explainable and accurate CAD mechanisms that establish a common understanding of visually important patterns for radiologists. The research involves developing a semantic deep-learning neural network (SDNN) that...
This federal Project Grant award of $680,433 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to The Regents of the University of California, San Francisco (UCSF) aims to apply advanced spatial proteogenomic and artificial intelligence technologies to improve prognostic estimation and understand the underlying biology driving pathology AI algorithms for prostate cancer. The key objectives are to: 1) explore the relationships between standard-of-care...
This federal Project Grant award of $718,456 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) supports research and development of an AI-powered system for automated analysis of abdominal CT scans to enhance detection and tracking of metastatic colorectal cancer. The key products and services to be delivered include: 1) Creating a large-scale...
This $989,122 Project Grant award from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to evaluate the performance of four commercial mammography-based artificial intelligence (AI) algorithms for breast cancer risk prediction in diverse U.S. screening populations. The project will assess the accuracy and equity of these AI risk models compared to traditional clinical risk factor-based models, using data from the Breast Cancer...
The National Cancer Institute (NCI) awarded a $1,199,514 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the Mayo Clinic Arizona to develop a comprehensive, fair, and scalable multimodal AI model called PRECISE that combines imaging and non-imaging data to enable early detection of pancreatic cancer. Key aims include: 1) developing deep learning models to segment imaging biomarkers from abdominal CT scans, 2) creating a fusion model using a graph neural...
This federal Project Grant award of $402,728 from the National Cancer Institute's Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to develop innovative interpretable deep learning models for multi-modality imaging in cancer prognostic assessment, with a focus on improving the accuracy and interpretability of prognosis predictions for gastric cancer patients. The funded research project at Wake Forest University Health Sciences will integrate domain knowledge from physician experts and disease pathobiology into deep learning models that leverage computed tomography (CT) imaging and whole-slide pathological images (WSIs) to enhance prognostic prediction capabilities. The goal is to create new tools that can improve gastric cancer treatment decisions without additional clinical costs, by providing more accurate and transparent prognosis predictions. The project period runs from July 2025 to June 2027.